plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | johndevitis/st7api-master | getBoundaryNodes.m | .m | st7api-master/code/util/getBoundaryNodes.m | 1,033 | utf_8 | 69b8cf9ebede06bdcd3c39fc46595af2 | %% getBoundaryNodes
% function to extract adn return boundary nodes from an x,y coordinate
% array.
%
% **note** assumes z coordinates = 0
%
% input:
% * coords = [n x 2] array of (x,y) coordinates.
%
% output:
% * bcoords = [n x 3] array of (x,y,z) coordinates. note that the function
% returns zeros for the z dime... |
github | johndevitis/st7api-master | getBounds.m | .m | st7api-master/code/util/getBounds.m | 1,028 | utf_8 | c7738233a266f453fd63c9b7ed81d9f7 | %% getBoundaryNodes
% function to extract adn return boundary nodes from an x,y coordinate
% array.
%
% **note** assumes z coordinates = 0
%
% input:
% * coords = [n x 2] array of (x,y) coordinates.
%
% output:
% * bcoords = [n x 3] array of (x,y,z) coordinates. note that the function
% returns zeros for the z dime... |
github | johndevitis/st7api-master | plotNSMassVsFreq.m | .m | st7api-master/code/util/plotNSMassVsFreq.m | 742 | utf_8 | ced291112c4662c683464fe045c673b9 | %% plotSectionVsFreq
%
% used for api sensitivity studies
%
% author: john braley
% create date: 13-Sep-2016
function plotNSMassVsFreq(results,field)
fh = figure('PaperPositionMode','auto');
ah = axes;
hold on
steps = length(results);
lins = {'+b','or','xg','*m'};
for jj = 1:resul... |
github | johndevitis/st7api-master | getPlateInfo.m | .m | st7api-master/code/@plate/getPlateInfo.m | 433 | utf_8 | 7c612d31e37bb4267ee39d659e0104a9 | %% getPlateInfo
%
%
%
% author: john devitis
% create date: 15-Aug-2016 12:02:38
function getPlateInfo(uID,propnum)
plate.material = getMaterialName(uID,propnum)
end
%% get material name by property number
function materialName = getMaterialName(uID,propnum)
global ptPLATEPROP
[iErr,materialname]... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | findDeskew.m | .m | Automated-Double-Pulse-master/findDeskew.m | 2,584 | utf_8 | a4795da1179114172af1816da3feae19 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | processWaveform.m | .m | Automated-Double-Pulse-master/processWaveform.m | 4,129 | utf_8 | c08a2877500902cf267ddf635f6bb5f5 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | waveformTimeIdx.m | .m | Automated-Double-Pulse-master/waveformTimeIdx.m | 852 | utf_8 | 294305a981f465fe5d70f030bc13fa95 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | SyncDoublePulseResults.m | .m | Automated-Double-Pulse-master/SyncDoublePulseResults.m | 33,216 | utf_8 | e7621123aa1e1687f332d7e262536f2e | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | Double_Pulse_Test.m | .m | Automated-Double-Pulse-master/Double_Pulse_Test.m | 8,246 | utf_8 | 245ebb5522e5a215e000bf1c5009dec0 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | pulse_generator.m | .m | Automated-Double-Pulse-master/pulse_generator.m | 1,433 | utf_8 | b166bc6ce75985745c3ff4abff104698 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | SettingsSweepObject.m | .m | Automated-Double-Pulse-master/SettingsSweepObject.m | 5,729 | utf_8 | 9c6731136d5adb019c8b306676d4056a | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | Synchronous_Double_Pulse_Test.m | .m | Automated-Double-Pulse-master/Synchronous_Double_Pulse_Test.m | 7,140 | utf_8 | c2d398d82d53b3a614c7647e85129dd0 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | DoublePulseResults.m | .m | Automated-Double-Pulse-master/DoublePulseResults.m | 42,718 | utf_8 | 56d5ec78d547a29bbbb591048b767c54 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | runDoublePulseTest.m | .m | Automated-Double-Pulse-master/runDoublePulseTest.m | 9,542 | utf_8 | 0a8a32a94a428c3dde84206d332a5ced | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | SimpleSettings.m | .m | Automated-Double-Pulse-master/SimpleSettings.m | 2,784 | utf_8 | bdbe4c5e87a62898f7f3fff1c746e668 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | extract_turn_on_waveform.m | .m | Automated-Double-Pulse-master/extract_turn_on_waveform.m | 2,631 | utf_8 | 43ef03ba579d579ef5c7f4eaacb3a46f | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | checkLoadInductor.m | .m | Automated-Double-Pulse-master/checkLoadInductor.m | 3,597 | utf_8 | 57e46157214189e6e2bba35d3241648e | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | splitWaveforms.m | .m | Automated-Double-Pulse-master/splitWaveforms.m | 4,447 | utf_8 | 1c65e8c864592032c79f5c1655d189fd | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | min2Scale.m | .m | Automated-Double-Pulse-master/min2Scale.m | 1,066 | utf_8 | 51710c7ec3e4f6b3d2cb76953a3a9c68 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | setVoltageToLoad.m | .m | Automated-Double-Pulse-master/setVoltageToLoad.m | 2,873 | utf_8 | 89ba6fe2d59a6adbb890b1bec1bd6c51 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | rescaleAndRepulse.m | .m | Automated-Double-Pulse-master/rescaleAndRepulse.m | 4,381 | utf_8 | 8edd0bd59e582f9da08a8d614f30b969 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | setVoltageToLoadForSynchronousDPT.m | .m | Automated-Double-Pulse-master/setVoltageToLoadForSynchronousDPT.m | 2,886 | utf_8 | 5d36c6d80a8577924ef164d1afc31060 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | Colorado-Power-Electronics-Center/Automated-Double-Pulse-master | extractWaveforms.m | .m | Automated-Double-Pulse-master/extractWaveforms.m | 1,281 | utf_8 | 48e2e226c418b495873b8151fc33d5a3 | %{
Part of the Automated Double Pulse Test Project
Copyright (C) 2017 Kyle Goodrick
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... |
github | hermesespinola/FOA-Kmeans-Color-Image-Segmentation-master | rosenbrock.m | .m | FOA-Kmeans-Color-Image-Segmentation-master/ObjectiveFunctions/rosenbrock.m | 373 | utf_8 | d58c4262c50715455f7c41b0466e5d24 | % Función de Rosenbrock%Erik Cuevas, Valentín Osuna-Enciso, Diego Oliva, Margarita Díaz
% Función que recibe un vector x n-dimensional
function fx = rosenbrock(x)%Número de dimensiones 2 para este problema
n = 2;sum = 0;
%Función de Rosenbrock
for j = 1:n-1;
sum = sum+100*(x(j)^2-x(j+1))^2+(x(j)-1)^2;
end
%Reg... |
github | jcbyts/pds-stimuli-master | delayedsaccadesNoise.m | .m | pds-stimuli-master/fromMarmieDemos/delayedsaccadesNoise.m | 13,726 | utf_8 | 76d78e21b6f874bc9fe12dd7b74ec0bc | function p = delayedsaccadesNoise(p)
%% Random seed
p = defaultBitNames(p);
p.defaultParameters.stimulus.randomNumberGenerater = 'mt19937ar';
p.trial.stimulus.rngs.sessionSeed=fix(1e6*sum(clock));
p.trial.stimulus.rngs.sessionRNG=RandStream(p.trial.stimulus.randomNumberGenerater, 'seed', p.trial.stimulus.rngs.sessionS... |
github | jcbyts/pds-stimuli-master | marmoviewCalibrationGrid.m | .m | pds-stimuli-master/fromMarmieDemos/marmoviewCalibrationGrid.m | 4,990 | utf_8 | 881b9f909b4b4a5cc8f538d28b1540d4 | function p = marmoviewCalibrationGrid(p)
%% Random seed
p = defaultBitNames(p);
p.defaultParameters.stimulus.randomNumberGenerater = 'mt19937ar';
p.trial.stimulus.rngs.sessionSeed=fix(1e6*sum(clock));
p.trial.stimulus.rngs.sessionRNG=RandStream(p.trial.stimulus.randomNumberGenerater, 'seed', p.trial.stimulus.rngs.sess... |
github | jcbyts/pds-stimuli-master | delayedsaccades.m | .m | pds-stimuli-master/fromMarmieDemos/delayedsaccades.m | 11,123 | utf_8 | 1cb8d2b1bcaa832a4c4f1715952135b0 | function p = delayedsaccades(p)
%% Random seed
p = defaultBitNames(p);
p.defaultParameters.stimulus.randomNumberGenerater = 'mt19937ar';
p.trial.stimulus.rngs.sessionSeed=fix(1e6*sum(clock));
p.trial.stimulus.rngs.sessionRNG=RandStream(p.trial.stimulus.randomNumberGenerater, 'seed', p.trial.stimulus.rngs.sessionSeed);... |
github | jcbyts/pds-stimuli-master | marmoview_FaceCal.m | .m | pds-stimuli-master/fromMarmieDemos/marmoview_FaceCal.m | 15,039 | utf_8 | 2551841d07cedfdb1994a2d238c478e4 | function p=marmoview_FaceCal(p,state,sn)
% MARMOVIEW FACE CALIBRATION
% simple manual eyetracker calibration
% this function will be called everytime PLDAPS updates time in the trial.
% Depending on the current state, different actions (defined below) happen
switch state
% Main action happen here
case p.t... |
github | jcbyts/pds-stimuli-master | trialSetup.m | .m | pds-stimuli-master/+stimuli/+dotmotion/trialSetup.m | 6,422 | utf_8 | da639a0c9f5ccc10ed9f49c5d974d264 | function trialSetup(p, sn)
if nargin < 2
sn = 'stimulus';
end
p.trial.pldaps.goodtrial = 1;
ppd = p.trial.display.ppd; % pixels per degree (linear approximation)
fps = p.trial.display.frate; % frames per second
ctr = p.trial.display.ctr(1:2); % center of the screen
% --- Set Fixation Point P... |
github | jcbyts/pds-stimuli-master | afterTrialFunction.m | .m | pds-stimuli-master/+marmoview/afterTrialFunction.m | 6,554 | utf_8 | a3a6a8e9c6b56db046147b1f02765d4f | function p=afterTrialFunction(p, state, sn)
if nargin<3
sn='marmoview';
end
switch state
case p.trial.pldaps.trialStates.trialSetup
hObj=MarmoView(1);%p.trial.(sn).
handles=guidata(hObj);
handles.OutputPanel.Visible = 'On';
handles.ParameterPanel.Visible = 'On'... |
github | jcbyts/pds-stimuli-master | refineCalibration.m | .m | pds-stimuli-master/+marmoview/refineCalibration.m | 8,952 | utf_8 | 123cecb9dc388854cbad5d0f5bc0025a | function c = refineCalibration(p)
if ~isa(p, 'pldaps')
calledFromMarmoView = true;
p = p.p;
else
calledFromMarmoView = false;
end
% --- Parameters
maxFrames = 100e3;
winRadius=50;
targDur=500;
targHold=50;
targFlash=20;
genNew=true;
iFrame=1;
ctr=p.trial.display.ctr(1:2);
dotSize=15;
% --- reset keyboard... |
github | smola/linguist-master | convert_variable.m | .m | linguist-master/samples/Matlab/convert_variable.m | 2,186 | utf_8 | 3d73feb0b3feaa01d8b434d83f275241 | function [name, order] = convert_variable(variable, output)
% Returns the name and order of the given variable in the output type.
%
% Parameters
% ----------
% variable : string
% A variable name.
% output : string.
% Either `moore`, `meijaard`, `data`.
%
% Returns
% -------
% name : string
% The variable name i... |
github | smola/linguist-master | create_ieee_paper_plots.m | .m | linguist-master/samples/Matlab/create_ieee_paper_plots.m | 34,238 | utf_8 | 3cf9c020f3fbd215ddc5182743c38bc8 | function create_ieee_paper_plots(data, rollData)
% Creates all of the figures for the IEEE paper.
%
% Parameters
% ----------
% data : structure
% A structure contating the data from generate_data.m for all of the bicycles
% and speeds for the IEEE paper.
% rollData : structure
% The data for a single bicycle at ... |
github | smola/linguist-master | plant.m | .m | linguist-master/samples/Matlab/plant.m | 2,087 | utf_8 | daf74d53d9253d37bd69d59c76021156 | function Yc = plant(varargin)
% function Yc = plant(varargin)
%
% Returns the system plant given a number.
%
% Parameters
% ----------
% varargin : variable
% Either supply a single argument {num} or three arguments {num1, num2,
% ratio}. If a single argument is supplied, then one of the six transfer
% functions ... |
github | visionjo/Agglomerative_Clustering-master | write_dmatrix.m | .m | Agglomerative_Clustering-master/matlab/utils/write_dmatrix.m | 1,656 | utf_8 | 464d2aba8e394247571f7b3e9d0c8f86 | %% Write Distance Matrix to File.
% Write distance matrix D to filepath fpath as a set of tuples, i.e.,
% (i,j,d), where i is row number, j is column number, and d is distance (or
% edge). D is assumed symmetric, unless specified otherwise via flag. Thus,
% only the upper triangle is scanned.
%
% File format is compat... |
github | visionjo/Agglomerative_Clustering-master | build_kdtrees.m | .m | Agglomerative_Clustering-master/matlab/utils/build_kdtrees.m | 866 | utf_8 | 157bfc856322391af84ac9e498531f55 | %% Prepares KD-Tree with k-NN for data matrix X.
% Provided a filepointer (i.e., fpath) and t
%
% @author Joseph P. Robinson
% @date 2016 July 25
%% Build
function IdxNN = build_kdtrees(fpath,k)
load(fpath, 'X');
[nsamples, ~] = size(X);
nclusters = nsamples; % each sample starts in own cluster
disp('##### Build Tre... |
github | visionjo/Agglomerative_Clustering-master | cluster_confusion.m | .m | Agglomerative_Clustering-master/matlab/+visualize/cluster_confusion.m | 4,412 | utf_8 | 53fd7e1bf932c3bfa5d92c799692ccee | %% Generate confusion matrix of clustering (pair-wise)
%
% cluster_ids - cluster labels (i.e., assignment)
% clabels - ground truth (i.e., class label)
%
% @author Joseph P. Robinson
% @date 2016 July 25
%% Build
function confusion = cluster_confusion(cluster_ids, clabels)
% generate confusion matrices
% RGB
co... |
github | visionjo/Agglomerative_Clustering-master | class_confusion.m | .m | Agglomerative_Clustering-master/matlab/+visualize/class_confusion.m | 4,608 | utf_8 | 4915a61ab4563b932688a10c609dfbcb | %% Generate confusion matrix of clustering (pair-wise)
%
% cluster_ids - cluster labels (i.e., assignment)
% clabels - ground truth (i.e., class label)
%
% @author Joseph P. Robinson
% @date 2016 July 25
%% Build
function confusion = class_confusion(cluster_ids, clabels)
% generate confusion matrices
% RGB
cols... |
github | visionjo/Agglomerative_Clustering-master | precision_vs_k.m | .m | Agglomerative_Clustering-master/matlab/+visualize/precision_vs_k.m | 1,434 | utf_8 | c1bfd88b1b0f36e339d07b6811ac4122 | %% Generate confusion matrix of clustering (pair-wise)
%
% precision - precision scores for various number of clusters
%
% kspan - (OPTIONAL) k values corresponding to precision. If not
% provided, k is assumed to span 1:length(precision)
%
% labels - (OPTIONAL) labels for legend.... |
github | visionjo/Agglomerative_Clustering-master | pairwise_recall.m | .m | Agglomerative_Clustering-master/matlab/+eval/pairwise_recall.m | 2,283 | utf_8 | b2a9b18522627350bfc31513b7a5907a | %%
% Determines pair-wise recall of the ith cluster w.r.t. clabels, i.e.,
% ground-truth is referenced to determine the observations from the same
% class (identity) and, hence, should be clustered together.
%
%
function stats = pairwise_recall(ids,clabels)
% get list of cluster IDs
bins = unique(ids);
k = length(bins... |
github | visionjo/Agglomerative_Clustering-master | pairwise_precision.m | .m | Agglomerative_Clustering-master/matlab/+eval/pairwise_precision.m | 1,601 | utf_8 | b8972a519f396afe8090bfafccb4faed | %%
% Determines pair-wise precision of the ith cluster w.r.t. clabels, i.e.,
% ground-truth is referenced to determine the observations from the same
% class (identity) and, hence, should be clustered together.
%
% stats.precision = TP / (TP + FP) for each class label
%
function stats = pairwise_precis... |
github | visionjo/Agglomerative_Clustering-master | pairwise_specificity.m | .m | Agglomerative_Clustering-master/matlab/+eval/pairwise_specificity.m | 1,984 | utf_8 | 03e8fe13b07371c9c18d5db7baa58bbc | %% Calculate pairwise specificity for clustering
%
%
% ids - cluster labels (i.e., assignment)
% clabels - ground truth (i.e., class label)
%
% specificity = TN / (FP + TN)
%
% @author Joseph P. Robinson
% @date 2016 July 25
%%
function stats = pairwise_specificity(ids,clabels)
%%
bins = unique(ids)... |
github | visionjo/Agglomerative_Clustering-master | eval_all.m | .m | Agglomerative_Clustering-master/matlab/+eval/eval_all.m | 1,533 | utf_8 | 5c6f97a748888870ab92c2277b013a5c | %% Load clustering stats.
%
% din - directories containing results; multiple directories are assumed to
% be different runs of the same experiment, i.e., results are averaged out
%
% @author Joseph P. Robinson
% @date 2016 July 25
%%
%% Build
function stats = eval_all(dirs_in, clabels, kspan)
nruns = length(dirs_in);
... |
github | visionjo/Agglomerative_Clustering-master | calculate_rank_order_distance.m | .m | Agglomerative_Clustering-master/matlab/rankorder/calculate_rank_order_distance.m | 2,636 | utf_8 | 7e915900226a2e238179ff3c4beec00c | %% Calculate rank-order distances.
% Construct distance matrix using rank order distance measure [1].
%
% $$d_m(a,b)=\sum_{i=0}^{min(O_a(b),k)} I_b(O_b(f_a(i)),k)$$
%
% $$D(a,b)=\frac{d_m(a,b) + d_m(b,a)}{min(O_a(b),O_b(a))}$$
% where $I_b$ is indicator fuction: 0 if NN is shared; else, 1.
%
% @param nn_ids - Nxk matr... |
github | visionjo/Agglomerative_Clustering-master | transitively_merge_clusters.m | .m | Agglomerative_Clustering-master/matlab/rankorder/transitively_merge_clusters.m | 1,396 | utf_8 | 2b92a9c444da1d51d5ea4d4783d6448b | %% Threshold rank-order distance matrix.
% Transitively step through matrix, merging each pairs w distances below
% threshold Eps into same cluster.
%
% Provided constraint matrix C, 'must-' and
%
% @param D - Rank order distance matrix
% @param Eps - Rank order distance threshold (i.e., epsilon) [default 1.6]
% @p... |
github | visionjo/Agglomerative_Clustering-master | rank_order.m | .m | Agglomerative_Clustering-master/matlab/rankorder/rank_order.m | 1,993 | utf_8 | fdabd3140c87acd04579e02c307079a1 | %% Rank-order clustering implementation.
% Transitively merge all pairs with distances below threshold using
% rank-order (RO) distance defined in [1].
%
% $$d_m(a,b)=\sum_{i=0}^{min(O_a(b),k)} I_b(O_b(f_a(i)),k)$$
%
% $$D(a,b)=\frac{d_m(a,b) + d_m(b,a)}{min(O_a(b),O_b(a))}$$
% where $I_b$ is indicator fuction: 0 if NN... |
github | tavildar/Polar-master | phi_x.m | .m | Polar-master/PolarM/GaussianApproximation/phi_x.m | 171 | utf_8 | a1833012e862567b547011890c1618fe |
function [y] = phi_x(x)
y = (x < 10) .* exp(-0.4527 * x.^(0.86) + 0.0218);
y = (x >= 10) .* sqrt(pi./x) .* (1 - 1.4286./(x + 0.0001)) .* exp(-x/4) + y;
% y = min(1, y); |
github | Smith-Jefferson/GranularRecommendation-master | RSGIF_Main.m | .m | GranularRecommendation-master/RSGIF_Main.m | 476 | utf_8 | 47c32c1ae66e4c9048f9f76d71289b06 | function RSGIF_Main
[data,testData]=dataFormat(0.9);
trainData=userPrefrence(data);
RSGIF_Magic=[];
matlabpool local 2;
for i=1:10
afa=myrandom(0.5,1);
beta=myrandom(0,0.5);
mae=RSGIF(trainData,testData,afa,beta);
maedat=mae(:,3);
maedat(isnan(maedat))=[];
RSGIF_Magic=[RSG... |
github | Smith-Jefferson/GranularRecommendation-master | RSGUF_Main.m | .m | GranularRecommendation-master/RSGUF_Main.m | 520 | utf_8 | 730e38315ad8ca06f53289fcb2106230 | function RSGUF_Main
[Tdata,TtestData]=dataSplit;
data=Tdata.ML_100_20;
testData=TtestData.ML_100_20;
trainData=userPrefrence(data);
RSGUF_Magic=[];
% matlabpool 2;
% parfor i=1:2
afa=0.4;
beta=myrandom(0,0.3);
mae=RSGUF(trainData,testData,afa,beta);
maedat=mae(:,3);
maedat(... |
github | spectm2/Albany-master | plot_xz_matlab.m | .m | Albany-master/examples/Aeras/XZHydrostatic/XZHydrostatic/plot_xz_matlab.m | 5,090 | utf_8 | f7c7d7d5bcbb3ffed3f503ed67b4f0a7 | %%%-----------------------------------------------------------
%%% Script to make contour plots of quantities remapped to z-surfaces
%%% from *exo file for XZHydrostatic.
%%% Matlab interface tools MEXNC ( url ) , etc. can be used
%%% as well as native Matlab functions.
%%% Note that MEXNC tools read vars in a native... |
github | spectm2/Albany-master | plot_errs.m | .m | Albany-master/examples/LCM/Schwarz/Cubes/RestartFullSchwarz/plot_errs.m | 613 | utf_8 | 7061daf7b64007a8075b483933579361 |
function [num_schwarz_iter, errs] = plot_errs(num_load_steps)
for i=1:num_load_steps
error_filenames = strcat('error_load',num2str(i-1),'_filenames');
errors = strcat('error_load',num2str(i-1),'_values');
err_order=dlmread(error_filenames)+1;
[X,I] = sort(err_order);
err = dlmread(errors); ... |
github | spectm2/Albany-master | vtk.m | .m | Albany-master/matlab/vtk.m | 8,718 | utf_8 | a1db10cc943c5921d20e61c20e4a51e3 | function varargout = vtk (varargin)
% Code for interacting with the tet meshes and solutions saved to VTK files
% by FMDB.
[varargout{1:nargout}] = feval(varargin{:});
end
% ------------------------------------------------------------------------------
% Public.
function ds = read_vtks (fn_base, nbrs, o)
iso;
o... |
github | XBTinChina/PRMLT-master | mixGaussEm.m | .m | PRMLT-master/chapter09/mixGaussEm.m | 2,256 | utf_8 | dc010412dc0a962e715166df5a2d3477 | function [label, model, llh] = mixGaussEm(X, init)
% Perform EM algorithm for fitting the Gaussian mixture model.
% Input:
% X: d x n data matrix
% init: k (1 x 1) number of components or label (1 x n, 1<=label(i)<=k) or model structure
% Output:
% label: 1 x n cluster label
% model: trained model struc... |
github | XBTinChina/PRMLT-master | mixGaussPred.m | .m | PRMLT-master/chapter09/mixGaussPred.m | 916 | utf_8 | 936d9c9031b78d903591b31113228e5c | function [label, R] = mixGaussPred(X, model)
% Predict label and responsibility for Gaussian mixture model.
% Input:
% X: d x n data matrix
% model: trained model structure outputed by the EM algirthm
% Output:
% label: 1 x n cluster label
% R: k x n responsibility
% Written by Mo Chen (sth4nth@gmail.com).
mu =... |
github | XBTinChina/PRMLT-master | mixBernEm.m | .m | PRMLT-master/chapter09/mixBernEm.m | 1,153 | utf_8 | 3c8f866ab93baeeda11b4815e8282940 | function [label, model, llh] = mixBernEm(X, k)
% Perform EM algorithm for fitting the Bernoulli mixture model.
% Input:
% X: d x n binary (0/1) data matrix
% k: number of cluster
% Output:
% label: 1 x n cluster label
% model: trained model structure
% llh: loglikelihood
% Written by Mo Chen (sth4nth@gmail.... |
github | XBTinChina/PRMLT-master | rvmBinEm.m | .m | PRMLT-master/chapter09/rvmBinEm.m | 2,127 | utf_8 | c4a86c6ab37cfc3ff17b556147785e4b | function [model, llh] = rvmBinEm(X, t, alpha)
% Relevance Vector Machine (ARD sparse prior) for binary classification.
% trained by empirical bayesian (type II ML) using EM.
% Input:
% X: d x n data matrix
% t: 1 x n label (0/1)
% alpha: prior parameter
% Output:
% model: trained model structure
% llh: loglik... |
github | XBTinChina/PRMLT-master | rvmBinFp.m | .m | PRMLT-master/chapter07/rvmBinFp.m | 2,178 | utf_8 | 3844c2907de5e6bf4b9ce12b4f1aebba | function [model, llh] = rvmBinFp(X, t, alpha)
% Relevance Vector Machine (ARD sparse prior) for binary classification.
% trained by empirical bayesian (type II ML) using Mackay fix point update.
% Input:
% X: d x n data matrix
% t: 1 x n label (0/1)
% alpha: prior parameter
% Output:
% model: trained model stru... |
github | XBTinChina/PRMLT-master | mixLogitBin.m | .m | PRMLT-master/chapter14/mixLogitBin.m | 1,347 | utf_8 | 2b3aebfe8ba22a64628d7dc83c3809c6 | function [model, llh] = mixLogitBin(X, t, k)
% Mixture of logistic regression model for binary classification optimized by Newton-Raphson method
% Input:
% X: d x n data matrix
% t: 1 x n label (0/1)
% k: number of mixture component
% Output:
% model: trained model structure
% llh: loglikelihood
% Written by ... |
github | XBTinChina/PRMLT-master | logitMn.m | .m | PRMLT-master/chapter04/logitMn.m | 2,124 | utf_8 | 99ad0d9aec803c53b72dfdbc191f30fb | function [model, llh] = logitMn(X, t, lambda)
% Multinomial regression for multiclass problem (Multinomial likelihood)
% Input:
% X: d x n data matrix
% t: 1 x n label (1~k)
% lambda: regularization parameter
% Output:
% model: trained model structure
% llh: loglikelihood
% Written by Mo Chen (sth4nth@gmail.c... |
github | XBTinChina/PRMLT-master | ld.m | .m | PRMLT-master/common/ld.m | 934 | utf_8 | bb0be7659f2bfe4ea5f10d93a6efba41 | % function [L, D] = ld(X)
% % LD factorization produces LDL'=X*X' which is the same as [L,D] = ldl(X*X');
% % the underlying algorithm is Gram-Schmidt orthogonalization
% [d,n] = size(X);
% m = min(d,n);
% L = eye(d,m);
% Q = zeros(m,n);
% D = zeros(m,1);
% for i = 1:m
% L(i,1:i-1) = X(i,:)*bsxfun(@times,Q(1:i-1,:)... |
github | XBTinChina/PRMLT-master | loggmpdf.m | .m | PRMLT-master/common/loggmpdf.m | 634 | utf_8 | f3dd90a736450ee6052a761044d23792 | function r = loggmpdf(X, model)
% Compute log pdf of a Gaussian mixture model.
% Written by Mo Chen (sth4nth@gmail.com).
mu = model.mu;
Sigma = model.Sigma;
w = model.weight;
n = size(X,2);
k = size(mu,2);
logRho = zeros(k,n);
for i = 1:k
logRho(i,:) = loggausspdf(X,mu(:,i),Sigma(:,:,i));
end
r = logsumexp(bsxfun... |
github | XBTinChina/PRMLT-master | mixGaussVb.m | .m | PRMLT-master/chapter10/mixGaussVb.m | 3,727 | utf_8 | a96f6dabe3b871cf3bc62cb6362779bc | function [label, model, L] = mixGaussVb(X, m, prior)
% Variational Bayesian inference for Gaussian mixture.
% Input:
% X: d x n data matrix
% m: k (1 x 1) or label (1 x n, 1<=label(i)<=k) or model structure
% Output:
% label: 1 x n cluster label
% model: trained model structure
% L: variational lower bound
%... |
github | XBTinChina/PRMLT-master | kalmanSmoother.m | .m | PRMLT-master/chapter13/LDS/kalmanSmoother.m | 2,514 | utf_8 | e9d4e3ed1fd008fc703da47cf7018a89 | function [nu, U, Ezz, Ezy, llh] = kalmanSmoother(X, model)
% Kalman smoother (forward-backward algorithm for linear dynamic system)
% Input:
% X: d x n data matrix
% model: model structure
% Output:
% nu: q x n matrix of latent mean mu_t=E[z_t] w.r.t p(z_t|x_{1:T})
% U: q x q x n latent covariance U_t=co... |
github | XBTinChina/PRMLT-master | kalmanFilter.m | .m | PRMLT-master/chapter13/LDS/kalmanFilter.m | 1,608 | utf_8 | 958207675e7ac0883e59179ee05c1865 | function [mu, V, llh] = kalmanFilter(X, model)
% Kalman filter
% Input:
% X: d x n data matrix
% model: model structure
% Output:
% mu: q x n matrix of latent mean mu_t=E[z_t] w.r.t p(z_t|x_{1:t})
% V: q x q x n latent covariance U_t=cov[z_t] w.r.t p(z_t|x_{1:t})
% llh: loglikelihood
% Written by Mo... |
github | XBTinChina/PRMLT-master | ldsEm.m | .m | PRMLT-master/chapter13/LDS/ldsEm.m | 1,183 | utf_8 | 2fbbe1c2e61d6bbfba92455e3f2c3ed9 | function [model, llh] = ldsEm(X, model)
% EM algorithm for parameter estimation of linear dynamic system.
% Input:
% X: d x n data matrix
% model: prior model structure
% Output:
% model: trained model structure
% llh: loglikelihood
% Written by Mo Chen (sth4nth@gmail.com).
tol = 1e-4;
maxIter = 100;
llh = -inf... |
github | itskov/MultiAnimalTrackerSuite-master | trackerCrawler.m | .m | MultiAnimalTrackerSuite-master/Misc/trackerCrawler.m | 2,360 | utf_8 | d266aa0133d1f38275dce0c00c970e68 | function [ ] = trackerCrawler( sourceDirectory, targetDirectory )
% First we're looking for movie files (*.mj2)
lister = FileLister(sourceDirectory,'*.avi');
videoFiles = lister.allFiles();
numberOfMovies = length(videoFiles);
% Then we're looking for Features files
lister = FileLister(sour... |
github | itskov/MultiAnimalTrackerSuite-master | AnimalsTracker.m | .m | MultiAnimalTrackerSuite-master/AnimalsTracker/AnimalsTracker.m | 34,555 | utf_8 | 8be9d26492b7bddf55c180b12a8c87b5 | function varargout = AnimalsTracker(varargin)
%ANIMALSTRACKER M-file for AnimalsTracker.fig
% ANIMALSTRACKER, by itself, c
% a new ANIMALSTRACKER or raises the existing
% singleton*.
%
% H = ANIMALSTRACKER returns the handle to a new ANIMALSTRACKER or the handle to
% the existing singleton*.
%
% ... |
github | rishemjit/CODO-master | graph.m | .m | CODO-master/graph.m | 503 | utf_8 | 6b03085b93648af4a7fe74b2e737f9d5 | % graph(a) is constructing a bi-partite graph for maxcut problem
% Input : nvars representing verticies of a graph
% Output : [A] is adjacency matrix [nvars x nvars]
function [A]=graph(a)
No_of_Vertex = a;
i=1;
for j = No_of_Vertex/2+1:No_of_Vertex-1
A(i,i+1) = 1;
A(i+1,i) = 1;
... |
github | rishemjit/CODO-master | euclideanDistance.m | .m | CODO-master/euclideanDistance.m | 943 | utf_8 | 9b2b79c580c486454fccc324e4ca0fbd | % euclideanDistance function calculates the eucledian distance between individuals.
% Input Parameters : [r,c] specifies particular individual.
% Output Parameters : distance matrix specifies calculated distance between
% individual and its neighbors.
function [distance] = euclideanDistance(r... |
github | rishemjit/CODO-master | codeWordfn.m | .m | CODO-master/codeWordfn.m | 658 | utf_8 | 9eaae7153efd8ee7fbe692f6b1948b80 | % codeWordfn divides the binary string into codewords of specified size
% Input : binary string and codeWord size
% Output : returns Codewords matrix [number of Codewords x size of Codeword]
function [codeWords]=codeWordfn(string,sizeCodeword)
No_of_Features=length(string);
% number of codewords
noCodeword... |
github | rishemjit/CODO-master | Hamming.m | .m | CODO-master/Hamming.m | 546 | utf_8 | 2349602afe7be32c3e7b5be570a4b2ff | % Hamming(x) function computes the hamming distance
% Input : Vector of individual's attitudes
% Output : scalar (fitness value) computed at a
function y = Hamming(inp)
% check to see the number of features
No_of_Features = length(inp);
centre1 = zeros(No_of_Features/2,1);
centre2 = ones(No_of_Features/2,1);
... |
github | rishemjit/CODO-master | NeighbourIndex.m | .m | CODO-master/NeighbourIndex.m | 1,005 | utf_8 | 38b66caa1810e37d01e4841743d78912 | % NeighbourIndex function finds individual's neighbours.
% Input Parameters : [r,c] specifies particular individual.
% Neighbourhood is set in options structure
% Output Parameters : [row_index1,row_index2,column_index1,column_index2]
% specifies indicies to retrieve neigh... |
github | rishemjit/CODO-master | MaxCut.m | .m | CODO-master/MaxCut.m | 1,469 | utf_8 | fd2c91622daf38fc1b2c2298c43d12f2 | % MaxCut(x) function is Maximum cut of a graph problem
% Input : Vector of individual's attitudes (1 x 1 x nvars)
% Output : scalar (fitness value) computed at a
function [y]= MaxCut(x)
% check to see the number of features
No_of_Features = size(x,3);
partitions = 2 ;
value = mod(No_of_Features,parti... |
github | rishemjit/CODO-master | Order3Deceptive.m | .m | CODO-master/Order3Deceptive.m | 1,091 | utf_8 | b2ff953b3ffb209ae05bd5c5989d5f30 | % order3deceptive(x) function is massively multimodal deceptive problem.
% Input : Vector of individual's attitudes
% Output : scalar (fitness value) computed at x
function [y]= Order3Deceptive(x)
% check to see the number of features
No_of_Features = length(x);
% Hamming_string to codeWords
if ~... |
github | rishemjit/CODO-master | Rastriginfn.m | .m | CODO-master/Rastriginfn.m | 878 | utf_8 | 7bcdcd5ca19c142da070a471e347236d | % rastriginfn(x) function is binary encoded continuous benchmark function using the
% precision 4 places after decimal point.
% Input : Vector of individual's attitudes
% Output : scalar (fitness value) computed at a
function [f]=Rastriginfn(a)
% check to see the number of features
No_of_Features... |
github | rishemjit/CODO-master | SitoOptimset.m | .m | CODO-master/SitoOptimset.m | 11,793 | utf_8 | 5bb845481ba34b87a33de6a3a726aab1 | function options = SitoOptimset(varargin)
% SITOOPTIMSET Create/alter SITO OPTIONS structure.
% SITOOPTIMSET returns a listing of the fields in the options structure as
% well as valid parameters and the default parameter.
%
% OPTIONS = SITOOPTIMSET('PARAM',VALUE) creates a structure with the
% defau... |
github | rishemjit/CODO-master | OneMax.m | .m | CODO-master/OneMax.m | 362 | utf_8 | 45d596c1c748cd7984a530ca45ec6a62 | % onemax(x) function counts the number of ones in the string
% Input : Vector of individual's attitudes
% Output : scalar (fitness value) computed at x
function [y]= OneMax(x)
% check to see the number of features
No_of_Features = length(x);
count_ones = sum( x );
count_zeros = No_of_Feature... |
github | rishemjit/CODO-master | Ecc.m | .m | CODO-master/Ecc.m | 1,079 | utf_8 | 5d8514369842cbeff7c2e825ac70bfad | % ecc(x) function is Error correcting code design problem in which minimum
% Hamming distance is maximized.
% Input : Vector of individual's attitudes (1 x 1 x nvars)
% Output : scalar (fitness value) computed at x
function [y]= Ecc(x)
% check to see the number of features
No_of_Features = size(x,3);
si... |
github | rishemjit/CODO-master | bin2decimal.m | .m | CODO-master/bin2decimal.m | 433 | utf_8 | 00f4b2f55516822ca85bf6dc5d9d10a0 | % bin2decimal(string) function converts the binary string to decimal value
% Input : row vector(binary string)
% Output : returns scalar(decimal value of string)
function [decimalValue]=bin2decimal(string)
decimalValue=0; count=0;
for x=length(string):-1:1
if string(x)==1
... |
github | rishemjit/CODO-master | bin2real.m | .m | CODO-master/bin2real.m | 677 | utf_8 | 91ed04330ce0cf9135fb3df40bf0e4e6 | % bin2real(codeWords,min,max) function converts the binary string to real
% value in the specified range
% Input : codeWords matrix [number of Codewords x size of Codeword] and range is specified
% Output : row vector of real values
function [realValue]=bin2real(codeWords,min,max)
sizeCodeword=size(cod... |
github | rishemjit/CODO-master | Bipolar.m | .m | CODO-master/Bipolar.m | 1,100 | utf_8 | 2d1dcde45bf36dcbefe9d4da7c697d94 | % bipolar(x) function is massively multimodal deceptive problem.
% Input : Vector of individual's attitudes (1 x 1 x nvars)
% Output : scalar (fitness value) computed at x
function [y]= Bipolar(x)
% check to see the number of features
No_of_Features = size(x,3);
sizeCodeword = 6;
value = mod( No_of_Feat... |
github | kjw0612/caffe-vdsr-master | cnn_cifar.m | .m | caffe-vdsr-master/Test/matconvnet/examples/cnn_cifar.m | 4,529 | utf_8 | e4063362a0a852098aab32182613a0b9 | function [net, info] = cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR-10
% The demo includes two standard model: LeNet and Network in
% Network (NIN). Use the 'modelType' option to choose one.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.modelT... |
github | kjw0612/caffe-vdsr-master | cnn_mnist_init.m | .m | caffe-vdsr-master/Test/matconvnet/examples/cnn_mnist_init.m | 2,385 | utf_8 | e4895ca0e40a022561d7a80114797bdb | function net = cnn_mnist_init(varargin)
% CNN_MNIST_LENET Initialize a CNN similar for MNIST
opts.useBnorm = true ;
opts = vl_argparse(opts, varargin) ;
rng('default');
rng(0) ;
f=1/100 ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{f*randn(5,5,1,20, 'single... |
github | kjw0612/caffe-vdsr-master | cnn_train_dag.m | .m | caffe-vdsr-master/Test/matconvnet/examples/cnn_train_dag.m | 10,207 | utf_8 | d45a3498f39f24060992f496d3b8fe39 | function stats = cnn_train_dag(net, imdb, getBatch, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
% the DagNN wrapper instead of the SimpleNN wrapper.
% Copyright (C) 2014-15 Andrea Vedaldi.
% All rights reserved.
%
% This... |
github | kjw0612/caffe-vdsr-master | cnn_imagenet_init.m | .m | caffe-vdsr-master/Test/matconvnet/examples/cnn_imagenet_init.m | 13,358 | utf_8 | e821a577e2d8ae838c896316cdb83203 | function net = cnn_imagenet_init(varargin)
% CNN_IMAGENET_INIT Initialize a standard CNN for ImageNet
opts.scale = 1 ;
opts.initBias = 0.1 ;
opts.weightDecay = 1 ;
%opts.weightInitMethod = 'xavierimproved' ;
opts.weightInitMethod = 'gaussian' ;
opts.model = 'alexnet' ;
opts.batchNormalization = false ;
opts = vl_argp... |
github | kjw0612/caffe-vdsr-master | cnn_imagenet.m | .m | caffe-vdsr-master/Test/matconvnet/examples/cnn_imagenet.m | 6,349 | utf_8 | 435507808f1606571239f6c62d25fa9b | function cnn_imagenet(varargin)
% CNN_IMAGENET Demonstrates training a CNN on ImageNet
% This demo demonstrates training the AlexNet, VGG-F, VGG-S, VGG-M,
% VGG-VD-16, and VGG-VD-19 architectures on ImageNet data.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataD... |
github | kjw0612/caffe-vdsr-master | cnn_mnist.m | .m | caffe-vdsr-master/Test/matconvnet/examples/cnn_mnist.m | 3,313 | utf_8 | f352cf83199c36c0e52cf0951fc5460c | function [net, info] = cnn_mnist(varargin)
% CNN_MNIST Demonstrated MatConNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.expDir = fullfile('data','mnist-baseline') ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.dataDir = fullfile('data','mnist') ;
o... |
github | kjw0612/caffe-vdsr-master | cnn_train.m | .m | caffe-vdsr-master/Test/matconvnet/examples/cnn_train.m | 14,363 | utf_8 | 486468acc54fcc9a36051389c2c534c5 | function [net, info] = cnn_train(net, imdb, getBatch, varargin)
%CNN_TRAIN An example implementation of SGD for training CNNs
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providing a suitable
... |
github | kjw0612/caffe-vdsr-master | cnn_imagenet_evaluate.m | .m | caffe-vdsr-master/Test/matconvnet/examples/cnn_imagenet_evaluate.m | 2,960 | utf_8 | 93a11af0121659362a46b7e80afe492b | function info = cnn_imagenet_evaluate(varargin)
% CNN_IMAGENET_EVALUATE Evauate MatConvNet models on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data', 'ILSVRC2012') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.imdbPat... |
github | kjw0612/caffe-vdsr-master | cnn_mnist_dag.m | .m | caffe-vdsr-master/Test/matconvnet/examples/cnn_mnist_dag.m | 3,786 | utf_8 | c219b46f0b6dee109dee36e456a6381a | function [net, info] = cnn_mnist_dag(varargin)
% CNN_MNIST Demonstrated MatConNet on MNIST using DAG
run(fullfile(fileparts(mfilename('fullpath')),...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.expDir = fullfile('data','mnist-baseline-dag') ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.dataDir = fullfile('... |
github | kjw0612/caffe-vdsr-master | vl_nnloss.m | .m | caffe-vdsr-master/Test/matconvnet/matlab/vl_nnloss.m | 9,569 | utf_8 | 2cdbefad14f4e37a525313830158381b | function Y = vl_nnloss(X,c,dzdy,varargin)
%VL_NNLOSS CNN categorical or attribute loss.
% Y = VL_NNLOSS(X, C) computes the loss incurred by the prediction
% scores X given the categorical labels C.
%
% The prediction scores X are organised as a field of prediction
% vectors, represented by a H x W x D x N array... |
github | kjw0612/caffe-vdsr-master | vl_argparse.m | .m | caffe-vdsr-master/Test/matconvnet/matlab/vl_argparse.m | 3,522 | utf_8 | fb3e14023af980ca3da7c79c7f952821 | function [opts, args] = vl_argparse(opts, args, varargin)
%VL_ARGPARSE Parse list of parameter-value pairs.
% OPTS = VL_ARGPARSE(OPTS, ARGS) updates the structure OPTS based on
% the specified parameter-value pairs ARGS={PAR1, VAL1, ... PARN,
% VALN}. The function produces an error if an unknown parameter name
% ... |
github | kjw0612/caffe-vdsr-master | vl_compilenn.m | .m | caffe-vdsr-master/Test/matconvnet/matlab/vl_compilenn.m | 25,151 | utf_8 | fe60dc90d21c4601fba12cfa1cc05b4a | function vl_compilenn(varargin)
%VL_COMPILENN Compile the MatConvNet toolbox.
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following options:
%... |
github | kjw0612/caffe-vdsr-master | getVarReceptiveFields.m | .m | caffe-vdsr-master/Test/matconvnet/matlab/+dagnn/@DagNN/getVarReceptiveFields.m | 3,549 | utf_8 | ca843d13890184e1451248f43f7d4011 | function rfs = getVarReceptiveFields(obj, var)
%GETVARRECEPTIVEFIELDS Get the receptive field of a variable
% RFS = GETVARRECEPTIVEFIELDS(OBJ, VAR) gets the receptivie fields RFS of
% all the variables of the DagNN OBJ into variable VAR. VAR is a variable
% name or index.
%
% RFS has one entry for each variable... |
github | kjw0612/caffe-vdsr-master | rebuild.m | .m | caffe-vdsr-master/Test/matconvnet/matlab/+dagnn/@DagNN/rebuild.m | 3,103 | utf_8 | fc57d8ce4b72dccf7227806ef718ff79 | function rebuild(obj)
%REBUILD Rebuild the internal data structures of a DagNN object
% REBUILD(obj) rebuilds the internal data structures
% of the DagNN obj. It is an helper function used internally
% to update the network when layers are added or removed.
varFanIn = zeros(1, numel(obj.vars)) ;
varFanOut = zero... |
github | kjw0612/caffe-vdsr-master | print.m | .m | caffe-vdsr-master/Test/matconvnet/matlab/+dagnn/@DagNN/print.m | 11,333 | utf_8 | d46d596afbf3bc5d368d0231b886d8a8 | function str = print(obj, inputSizes, varargin)
%PRINT Print information about the DagNN object
% PRINT(OBJ) displays a summary of the functions and parameters in the network.
% STR = PRINT(OBJ) returns the summary as a string instead of printing it.
%
% PRINT(OBJ, INPUTSIZES) where INPUTSIZES is a cell array of ... |
github | kjw0612/caffe-vdsr-master | fromSimpleNN.m | .m | caffe-vdsr-master/Test/matconvnet/matlab/+dagnn/@DagNN/fromSimpleNN.m | 8,666 | utf_8 | 49611ecd8024663169c585f241a52325 | function obj = fromSimpleNN(net, varargin)
% FROMSIMPLENN Initialize a DagNN object from a SimpleNN network
% FROMSIMPLENN(NET) initializes the DagNN object from the
% specified CNN using the SimpleNN format.
%
% SimpleNN objects are linear chains of computational layers. These
% layers echange information thr... |
github | kjw0612/caffe-vdsr-master | vl_simplenn_display.m | .m | caffe-vdsr-master/Test/matconvnet/matlab/simplenn/vl_simplenn_display.m | 11,523 | utf_8 | 9e71a773ec012daa995420a911fd9be9 | function [info, str] = vl_simplenn_display(net, varargin)
% VL_SIMPLENN_DISPLAY Simple CNN statistics
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
%
% INFO=VL_SIMPLENN_DISPLAY(NET) returns instead a structure INFO
% with several statistics for each layer of the network NET.
%
% The f... |
github | kjw0612/caffe-vdsr-master | vl_test_economic_relu.m | .m | caffe-vdsr-master/Test/matconvnet/matlab/xtest/vl_test_economic_relu.m | 790 | utf_8 | 35a3dbe98b9a2f080ee5f911630ab6f3 | % VL_TEST_ECONOMIC_RELU
function vl_test_economic_relu()
x = randn(11,12,8,'single');
w = randn(5,6,8,9,'single');
b = randn(1,9,'single') ;
net.layers{1} = struct('type', 'conv', ...
'filters', w, ...
'biases', b, ...
'stride', 1, ...
... |
github | hangong/deshadow-master | freehanddraw.m | .m | deshadow-master/freehanddraw.m | 3,237 | utf_8 | d2a52b800bb8f105188826c050feccf1 | function [lineobj,xs,ys] = freehanddraw(varargin)
% [LINEOBJ,XS,YS] = FREEHANDDRAW(ax_handle,line_options)
%
% Draw a smooth freehand line object on the current axis (default),
% or on the axis specified by handle in the first input argument.
% Left-click to begin drawing, right-click to terminate, or double-click... |
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